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Concept learning

Concept learning. Maria Simi, 2012/2013 Machine Learning, Tom Mitchell Mc Graw -Hill International Editions, 1997 ( Cap 1, 2). Introduction to machine learning. Introduction to machine learning When appropriate and when not appropriate Task definition

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Concept learning

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  1. Concept learning Maria Simi, 2012/2013 Machine Learning, Tom Mitchell McGraw-Hill International Editions, 1997 (Cap 1, 2).

  2. Introduction to machine learning • Introduction to machine learning • When appropriate and when not appropriate • Task definition • Learning methodology: design, experiment, evaluation • Learning issues: representing hypothesis • Learning paradigms • Supervised learning • Unsupervised learning • Reinforcement learning

  3. AIMA learning architecture

  4. Machine learning: definition • A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E [Mitchell] • Problem definition for a learning agent • Task T • Performance measure P • Experience E

  5. Designing a learning system • Choosing the training experience • Examples of best moves, games outcome … • Choosing the target function • board-move, board-value, … • Choosing a representation for the target function • linear function with weights (hypothesis space) • Choosing a learning algorithm for approximating the target function • A method for parameter estimation

  6. Design of a learning system Mitchell

  7. Inductive learning • Inductive learning • Inducing a general function from training examples • A supervised paradigm • Basic schemas that assume a logical representation of the hypothesis • Concept learning • Decision trees learning • Important issues • Inductive bias (definition) • The problem of overfitting • Bibliography: • Mitchell, cap1,2,3

  8. Definition of concept learning • Task: learning a category description (concept) from a set of positive and negative training examples. • Concept may be a set of events, objects … • Target function: a boolean function c: X {0, 1} • Experience: a set of training instances D:{x, c(x)} • A search problem for best hypothesis in a hypotheses space • The space is determined by the choice of representation of the hypothesis (all boolean functions or a subset)

  9. Sport example • Concept to be learned: Days in which Aldo can enjoy water sport Attributes: Sky: Sunny, Cloudy, Rainy Wind: Strong, Weak AirTemp: Warm, Cold Water: Warm, Cool Humidity: Normal, High Forecast: Same, Change • Instances in the training set (out of the 96 possible):

  10. Hypotheses representation • h is a set of constraints on attributes: • a specific value: e.g. Water = Warm • any value allowed: e.g. Water = ? • no value allowed: e.g. Water = Ø • Example hypothesis: Sky AirTemp Humidity Wind Water Forecast Sunny, ?, ?, Strong, ?, Same Corresponding to boolean function: Sky=Sunny ∧ Wind=Strong ∧ Forecast=Same • H, hypotheses space, all “representable”h

  11. Hypothesis satisfaction • An instance xsatisfies an hypothesis h iffall the constraints expressed by h are satisfied by the attribute values in x. • Example 1: x1: Sunny, Warm, Normal, Strong, Warm, Same h1: Sunny, ?, ?, Strong, ?, Same Satisfies? Yes • Example 2: x2: Sunny, Warm, Normal, Strong, Warm, Same h2: Sunny, ?, ?, Ø, ?, Same Satisfies? No

  12. Formal task description • Given: • X all possible days, as described by the attributes • A set of hypothesis H, a conjunction of constraints on the attributes, representing a function h: X  {0, 1} [h(x) = 1 if x satisfies h; h(x) = 0 if x does not satisfy h] • A target concept: c: X {0, 1} where c(x) = 1 iff EnjoySport = Yes; c(x) = 0 iff EnjoySport = No; • A training set of possible instances D: {x, c(x)} • Goal: find a hypothesis h in H such that h(x) = c(x) for all x in X Hopefully h will be able to predict outside D…

  13. The inductive learning assumption • We can at best guarantee that the output hypothesis fits the target concept over the training data • Assumption: an hypothesis that approximates well the training data will also approximate the target function over unobserved examples • i.e. given a significant training set, the output hypothesis is able to make predictions

  14. Concept learning as search • Concept learning is a task of searching an hypotheses space • The representation chosen for hypotheses determines the search space • In the example we have: • 3 x 25 = 96 possible instances (6 attributes) • 1 + 4 x 35= 973 possible hypothesis considering that all the hypothesis with some  are semantically equivalent, i.e. inconsistent • Structuring the search space may help in searching more efficiently

  15. General to specific ordering • Consider: h1 =Sunny, ?, ?, Strong, ?, ? h2 =Sunny, ?, ?, ?, ?, ? • Any instance classified positive by h1 will also be classified positive by h2 • h2 is more general than h1 • Definition: hjghkiff (x  X ) [(hk=1) (hj=1)] g more general or equal; >g strictly more general • Most general hypothesis: ?, ?, ?, ?, ?, ? • Most specific hypothesis: Ø, Ø, Ø, Ø, Ø, Ø

  16. General to specific ordering: induced structure

  17. Find-S: finding the most specific hypothesis • Exploiting the structure we have alternatives to enumeration … • Initialize h to the most specific hypothesis in H • For each positive training instance: for each attribute constraint ain h: If the constraint ais satisfied by xthen do nothing elsereplace ain h by the next more general constraint satified by x(move towards a more general hp) • Output hypothesish

  18. Find-S in action

  19. Properties of Find-S • Find-S is guaranteed to output the most specific hypothesis within H that is consistent with the positive training examples • The final hypothesis will also be consistent with the negative examples • Problems: • There can be more than one “most specific hypotheses” • We cannot say if the learner converged to the correct target • Why choose the most specific? • If the training examples are inconsistent, the algorithm can be mislead: no tolerance to rumor. • Negative example are not considered

  20. Candidate elimination algorithm: the idea • The idea: output a description of the set of all hypothesesconsistentwith the training examples (correctly classify training examples). • Version space: a representation of the set of hypotheseswhich are consistent with D • an explicit list of hypotheses (List-Than-Eliminate) • a compact representation of hypotheses which exploits the more_general_than partial ordering (Candidate-Elimination)

  21. Version space • The version space VSH,Dis the subset of the hypothesis from H consistent with the training example in D VSH,D {h  H | Consistent(h, D)} • An hypothesis his consistent with a set of training examples Diff h(x) = c(x) for each exampleinD Consistent(h, D)  ( x, c(x)  D) h(x) = c(x)) Note: "xsatisfiesh" (h(x)=1) different from “hconsistent with x" In particular when an hypothesis h is consistent with a negative example d =x, c(x)=No, then x must not satisfy h

  22. The List-Then-Eliminate algorithm Version space as list of hypotheses • VersionSpace  a list containing every hypothesis in H • For each training example, x, c(x) Remove from VersionSpaceany hypothesish for which h(x) c(x) • Output the list of hypotheses in VersionSpace • Problems • The hypothesis space must be finite • Enumeration of all the hypothesis, rather inefficient

  23. A compact representation for Version Space Note: The output of Find-S is just Sunny, Warm, ?, Strong, ?, ? • Version space represented by its most general members G and its most specific members S (boundaries)

  24. General and specific boundaries • The Specific boundary, S, of version space VSH,Dis the set of its minimally general (most specific) members S {s  H | Consistent(s, D)(s'  H)[(s gs') Consistent(s', D)]} Note: any member of S is satisfied by all positive examples, but more specific hypotheses fail to capture some • The General boundary, G, of version space VSH,Dis the set of its maximally general members G {g  H | Consistent(g, D)(g'  H)[(g' gg) Consistent(g', D)]} Note: any member of G is satisfied by no negative example but more general hypothesis cover some negative example

  25. Version Space representation theorem • G and S completely define the Version Space • Theorem: Every member of the version space (h consistent with D) is in S or G or lies between these boundaries VSH,D={h  H |(s  S) (g  G) (g g h g s)} where x gy means x is more general or equal to y Sketch of proof: Ifg g h g s,since sis in S and h g s, h is satisfied by all positive examples in D;gis in G and g g h, then h is satisfied by no negative examples inD; therefore hbelongs toVSH,D  It can be proved by assuming a consistent h that does not satisfy the right-hand side and by showing thatthiswould lead to a contradiction

  26. Candidate elimination algorithm-1 S  minimally general hypotheses in H, G maximally general hypotheses in H Initially any hypothesis is still possible S0 =, , , , ,  G0=?, ?, ?, ?, ?, ? For each training example d, do: If d is a positive example: • Remove from G any hinconsistent with d • Generalize(S, d) If d is a negative example: • Remove from S any hinconsistent with d • Specialize(G, d) Note:when d =x, No is a negative example, an hypothesis h is inconsistent with diffh satisfiesx

  27. Candidate elimination algorithm-2 Generalize(S, d): dis positive For each hypothesis s in S not consistent with d: • Remove s from S • Add to S all minimal generalizations of s consistent with d and having a generalization inG • Remove from S any hypothesis with a more specific h in S Specialize(G, d): dis negative For each hypothesis g in G not consistent with d: i.e. g satisfies d, • Remove g from G but d is negative • Add to G all minimal specializations of g consistent with dand having a specialization inS • Remove from G any hypothesis having a more general hypothesis in G

  28. Example: initially S0: , , , , .  ?, ?, ?, ?, ?, ? G0

  29. Example: after seingSunny,Warm, Normal, Strong, Warm, Same  + S0: , , , , .  S1: Sunny,Warm, Normal, Strong, Warm, Same ?, ?, ?, ?, ?, ? G0, G1

  30. Example: after seingSunny,Warm, High, Strong, Warm, Same + S1: Sunny,Warm, Normal, Strong, Warm, Same S2: Sunny,Warm, ?, Strong, Warm, Same ?, ?, ?, ?, ?, ? G1, G2

  31. Example: after seingRainy, Cold, High, Strong, Warm, Change  S2, S3: Sunny, Warm, ?, Strong, Warm, Same G3: Sunny, ?, ?, ?, ?, ??, Warm, ?, ?, ?, ??, ?, ?, ?, ?, Same ?, ?, ?, ?, ?, ? G2:

  32. Example: after seingSunny, Warm, High, Strong, Cool Change  + S3 Sunny, Warm, ?, Strong, Warm, Same S4 Sunny, Warm, ?, Strong, ?, ? G4: Sunny, ?, ?, ?, ?, ??, Warm, ?, ?, ?, ? Sunny, ?, ?, ?, ?, ??, Warm, ?, ?, ?, ??, ?, ?, ?, ?, Same G3:

  33. Learned Version Space

  34. Observations • The learned Version Space correctly describes the target concept, provided: • There are no errors in the training examples • There is some hypothesis that correctly describes the target concept • If S and G converge to a single hypothesis the concept is exactly learned • In case of errors in the training, useful hypothesis are discarded, no recovery possible • An empty version space means no hypothesis in H is consistent with training examples

  35. Ordering on training examples • The learned version space does not change with different orderings of training examples • Efficiency does • Optimal strategy (if you are allowed to choose) • Generate instances that satisfy half the hypotheses in the current version space. For example: Sunny, Warm, Normal, Light, Warm, Same satisfies 3/6 hyp. • Ideally the VS can be reduced by half at each experiment • Correct target found in log2|VS| experiments

  36. Classified as positive by all hypothesis, since satisfies any hypothesis in S Use of partially learned concepts

  37. Classifying new examples Classified as negative by all hypothesis, since does not satisfy any hypothesis in G

  38. Classifying new examples Uncertain classification: half hypothesis are consistent, half are not consistent

  39. Classifying new examples Sunny, Cold, Normal, Strong, Warm, Same 4 hypothesis not satisfied; 2 satisfied Probably a negative instance. Majority vote?

  40. Questions • What if H does not contain the target concept? • Can we improve the situation by extending the hypothesis space? • Will this influence the ability to generalize? • These are general questions for inductive inference, addressed in the context of Candidate-Elimination • Suppose we include in H every possible hypothesis … including the ability to represent disjunctive concepts

  41. Extending the hypothesis space • No hypothesis consistent with the three examples with the assumption that the target is a conjunction of constraints ?, Warm, Normal, Strong, Cool, Change is too general • Target concept exists in a different space H', including disjunction and in particular the hypothesis Sky=Sunny or Sky=Cloudy • Removing the bias …

  42. An unbiased learner • Every possible subset of X is a possible target |H'| = 2|X|, or 296 (vs |H| = 973, a strong bias) • This amounts to allowing conjunction, disjunction and negation Sunny, ?, ?, ?, ?, ? V <Cloudy, ?, ?, ?, ?, ? Sunny(Sky) V Cloudy(Sky) • We are guaranteed that the target concept exists • No generalization is however possible!!! Let's see why …

  43. A bad learner • VS after presenting three positive instances x1, x2, x3, and two negative instances x4, x5 S = {(x1 v x2 v x3)} G = {¬(x4 v x5)} … all subsets including x1 x2 x3 and not including x4 x5 • We can only classify precisely examples already seen! • Take a majority vote? Impossible … • Unseen instances, e.g. x, are classified positive (and negative) by half of the hypothesis • For any hypothesis h that classifies x as positive, there is a complementary hypothesis ¬h that classifies x as negative

  44. No inductive inference without a bias • A learner that makes no a priori assumptions regarding the identity of the target concept, has no rational basis for classifying unseen instances • The inductive bias of a learner are the assumptions that justify its inductive conclusions or the policy adopted for generalization • Different learners can be charact erized by their bias

  45. Inductive bias: definition • Given: • a concept learning algorithm L for a set of instances X • a concept c defined over X • a set of training examples for c: Dc = {x, c(x)} • L(xi, Dc) outcome of classification of xi after learning • Inductive inference ( ≻ ): Dc xi≻L(xi, Dc) • The inductive bias is defined as a minimal set of assumptions B, such that (|− for deduction)  (xi X) [ (B  Dc xi)|−L(xi, Dc) ]

  46. Inductive bias of Candidate-Elimination • Assume L is defined as follows: • compute VSH,D • classify new instance by complete agreement of all the hypotheses in VSH,D • Then the inductive bias of Candidate-Elimination is simply B (c  H) • In fact by assuming c  H: • c  VSH,D , in fact VSH,D includes all hypotheses in H consistent with D • L(xi, Dc) outputs a classification "by complete agreement", hence any hypothesis, including c, outputs L(xi, Dc)

  47. Inductive system

  48. Equivalent deductive system

  49. Each learner has an inductive bias • Three learner with three different inductive bias: • Rote learner: no inductive bias, just stores examples and is able to classify only previously observed examples • CandidateElimination: the concept c is in H and is a conjunction of constraints • Find-S: the concept c is in H, is a conjunction of constraints plus "all instances are negative unless seen as positive examples” (stronger bias) • The stronger the bias, greater the ability to generalize and classify new instances (greater inductive leaps).

  50. Bibliography • Machine Learning, Tom Mitchell, McGraw-Hill International Editions, 1997 (Cap 2).

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